TY - JOUR
T1 - Combinatorics-based approaches to controllability characterization for bilinear systems
AU - Cheng, Gong
AU - Zhang, Wei
AU - Li, Shin
N1 - Funding Information:
∗Received by the editors September 8, 2020; accepted for publication (in revised form) June 27, 2021; published electronically October 11, 2021. https://doi.org/10.1137/20M1365351 Funding: This work was supported in part by the National Science Foundation under awards CMMI-1933976 and ECCS-1810202, and by the Air Force Office of Scientific Research under award FA9550-21-1-0335. †Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130 USA (gong.cheng@wustl.edu, wei.zhang@wustl.edu). ‡Corresponding author. Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130 USA (jsli@wustl.edu).
Publisher Copyright:
© 2021 Society for Industrial and Applied Mathematics Publications. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The control of bilinear systems has attracted considerable attention in the field of systems and control for decades, owing to their prevalence in diverse applications across science and engineering disciplines. Although much work has been conducted on analyzing controllability properties, the most used tool remains the Lie algebra rank condition. In this paper, we develop alternative approaches based on theory and techniques in combinatorics to study controllability of bilinear systems. The core idea of our methodology is to represent vector fields of a bilinear system by permutations or graphs, so that Lie brackets are represented by permutation multiplications or graph operations, respectively. Following these representations, we derive combinatorial characterization of controllability for bilinear systems, which consequently provides novel applications of symmetric group and graph theory to control theory. Moreover, the developed combinatorial approaches are compatible with Lie algebra decompositions, including the Cartan and nonintertwining decomposition. This compatibility enables the exploitation of representation theory for analyzing controllability, which allows us to characterize controllability properties of bilinear systems governed by semisimple and reductive Lie algebras 2021 Society for Industrial and Applied Mathematics.
AB - The control of bilinear systems has attracted considerable attention in the field of systems and control for decades, owing to their prevalence in diverse applications across science and engineering disciplines. Although much work has been conducted on analyzing controllability properties, the most used tool remains the Lie algebra rank condition. In this paper, we develop alternative approaches based on theory and techniques in combinatorics to study controllability of bilinear systems. The core idea of our methodology is to represent vector fields of a bilinear system by permutations or graphs, so that Lie brackets are represented by permutation multiplications or graph operations, respectively. Following these representations, we derive combinatorial characterization of controllability for bilinear systems, which consequently provides novel applications of symmetric group and graph theory to control theory. Moreover, the developed combinatorial approaches are compatible with Lie algebra decompositions, including the Cartan and nonintertwining decomposition. This compatibility enables the exploitation of representation theory for analyzing controllability, which allows us to characterize controllability properties of bilinear systems governed by semisimple and reductive Lie algebras 2021 Society for Industrial and Applied Mathematics.
KW - Bilinear systems
KW - Cartan decomposition
KW - Graph theory
KW - Lie groups
KW - Representation theory
KW - Symmetric groups
UR - http://www.scopus.com/inward/record.url?scp=85117885678&partnerID=8YFLogxK
U2 - 10.1137/20M1365351
DO - 10.1137/20M1365351
M3 - Article
AN - SCOPUS:85117885678
SN - 0363-0129
VL - 59
SP - 3574
EP - 3599
JO - SIAM Journal on Control and Optimization
JF - SIAM Journal on Control and Optimization
IS - 5
ER -